learning - Academic Conferences Limited

When Agents Make Suggestions About Readings
Orlando Belo
Algoritmi R&D Centre, University of Minho, Portugal
obelo@di.uminho.pt
Abstract: Significant efforts have been made during the last few years in the design and implementation of
pedagogical agents for a wide range of application domains. One of the most common target area is the
assistance to students in cases of regular subject studying, promoting means that help them to improve their
performance and expertise in some specific subject areas. Frequently students ask their teachers about the
“best” and more effective bibliographic resources that they could use to study and validate knowledge for some
working topics. In this paper we will discuss the basic characteristics of pedagogical agents, approaching their
typical functional architecture, and services, reinforcing the discussion on a specific class of pedagogical agents
that are responsible to support students during their studying sessions, helping them in the validation of their
knowledge, suggesting bibliographic resources information whenever requested.
Keywords: eLearning platforms, agent based computing, intelligent tutoring systems, software agents, artificial
intelligent tutors, bibliographic resources suggestion
1. Introduction
Agent based applications are very appellative. Software agents have been used to support a lot of
tasks in real world applications (Van der Hoek & Wooldridge 2008). Ranging from telecommunications
to retail, or doing monitoring services on hydroelectric power plants, agent based computing has been
always a very good asset in a lot of problem solving arenas. eLearning is no exception to this
attractive paradigm and to all of its characteristics and potentialities (Agarwal et al. 2004) (Leung & Li
2001). One of the most relevant agent applications on this field was in the design and development of
pedagogical agents, normally designed by artificial tutors or intelligent assistants. Basically, these
entities are conceived to ensure more effective tutoring services, in some very specialized areas of
studying, having clearly pedagogical purposes, giving assistance to students in cases of regular
subject studying or even doing evaluation tasks. Additionally, they are also able to perform
administrative and optimization services inside eLearning platforms, supervising what users are doing
and suggesting better ways to do it or recommending particular resources that can help them in
current tasks.
The use of software agents as sophisticated autonomous means helping students on bibliographic
resource selection seems to be very useful and appellative. The ability that agents have to adapt to
new scenarios and to communicate with other means of learning makes possible a very dynamic
eLearning environment, where students needs can be satisfied easily. An agent can also adapt in real
time different plans of readings for current user needs, finding the best solution for a given studying
plan, and personalizing processes and exploitation scenarios (Schiaffino et al. 2008). To do that, they
simply need to act as search engines over their databases looking for a list of references that satisfies
student current, his preferences, and a previous plan of readings prepared by his teachers. This
means that any assistant agent (Okamoto et al. 2009) must establish usage profiles and accordingly
prepare its plan of action an user satisfaction. It is very desirable that the processes of readings
suggestion is versatile and proactive, providing the references that students require and, at the same
time, giving viable alternatives that follow other suggestion indicators (e.g. readings usage ranking,
recommended references, teacher’s preferences, or external sources identification).
In this paper we will focus our attention on a specific class of pedagogical agents that are responsible
to support students during their studying sessions, helping them in the validation of their knowledge in
a particular domain, and (as their priority goal) suggesting bibliographic resources information
whenever requested or inferred as necessary during an evaluation process, accordingly current
studying status of the students. We will discuss the basic characteristics of these pedagogical agents
(section 2), reinforcing the discussion presenting an application domain for bibliographic resource
suggestion in a conventional eLearning scenario (section 3), and present the basic characteristics and
functional architecture of a specific software agent for personalised assistance in bibliographic
resource suggestion (section 4). Finally, in section 5, we will present some conclusions and future
work.
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